1987
DOI: 10.1086/114383
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Time Series Analysis with Clean - Part One - Derivation of a Spectrum

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Cited by 809 publications
(671 citation statements)
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“…The agreement between the two periods is excellent. These values are confirmed with the clean algorithm (Roberts et al 1987), which removes spurious periods caused by the window function. The Lomb-Scargle periodogram in a range of frequencies 0 -10 d −1 and an inset of the clean periodogram, up to ν = 5 d −1 are shown in Figure 4.…”
Section: Period Determinationsupporting
confidence: 61%
“…The agreement between the two periods is excellent. These values are confirmed with the clean algorithm (Roberts et al 1987), which removes spurious periods caused by the window function. The Lomb-Scargle periodogram in a range of frequencies 0 -10 d −1 and an inset of the clean periodogram, up to ν = 5 d −1 are shown in Figure 4.…”
Section: Period Determinationsupporting
confidence: 61%
“…In addition, the standard deviation within each lightcurve as a function of the object brightness can be used to derive a second variability index. Sinusoidal signals are identified by a combination of the Scargle-Lomb periodogram (Scargle 1982) and the CLEAN algorithm (Roberts et al 1987). Identified binaries can be analysed using the DEBIL/MECI programs (Devor 2005 andDevor &Charbonneau 2006).…”
Section: Discussionmentioning
confidence: 99%
“…The two next highest peaks (indicated with arrows) turn out to be 1 day aliases. To illustrate this, we have applied the CLEAN algorithm as implemented by Roberts et al (1987) to remove the effects of the window function. The second panel of Figure 5 shows that only the 0.837 day peak survives this process, suggesting that it is a real signal.…”
Section: Study Of the Light-curve Residualsmentioning
confidence: 99%